{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import PySimpleGUI as sg\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_table():\n",
    "\n",
    "    sg.set_options(auto_size_buttons=True)\n",
    "    filename = sg.popup_get_file(\n",
    "        'Dataset to read',\n",
    "        title='Dataset to read',\n",
    "        no_window=True, \n",
    "        file_types=((\"CSV Files\", \"*.csv\"),(\"Text Files\", \"*.txt\")))\n",
    "    # --- populate table with file contents --- #\n",
    "    if filename == '':\n",
    "        return\n",
    "\n",
    "    data = []\n",
    "    header_list = []\n",
    "    colnames_prompt = sg.popup_yes_no('Does this file have column names already?')\n",
    "    nan_prompt = sg.popup_yes_no('Drop NaN entries?')\n",
    "\n",
    "    if filename is not None:\n",
    "        fn = filename.split('/')[-1]\n",
    "        try:                     \n",
    "            if colnames_prompt == 'Yes':\n",
    "                df = pd.read_csv(filename, sep=',', engine='python')\n",
    "                # Uses the first row (which should be column names) as columns names\n",
    "                header_list = list(df.columns)\n",
    "                # Drops the first row in the table (otherwise the header names and the first row will be the same)\n",
    "                data = df[1:].values.tolist()\n",
    "            else:\n",
    "                df = pd.read_csv(filename, sep=',', engine='python', header=None)\n",
    "                # Creates columns names for each column ('column0', 'column1', etc)\n",
    "                header_list = ['column' + str(x) for x in range(len(df.iloc[0]))]\n",
    "                df.columns = header_list\n",
    "                # read everything else into a list of rows\n",
    "                data = df.values.tolist()\n",
    "            # NaN drop?\n",
    "            if nan_prompt=='Yes':\n",
    "                df = df.dropna()\n",
    "            \n",
    "            return (df,data, header_list,fn)\n",
    "        except:\n",
    "            sg.popup_error('Error reading file')\n",
    "            return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_table(data, header_list, fn):\n",
    "    layout = [\n",
    "        [sg.Table(values=data,\n",
    "                  headings=header_list,\n",
    "                  font='Helvetica',\n",
    "                  pad=(25,25),\n",
    "                  display_row_numbers=False,\n",
    "                  auto_size_columns=True,\n",
    "                  num_rows=min(25, len(data)))]\n",
    "    ]\n",
    "\n",
    "    window = sg.Window(fn, layout, grab_anywhere=False)\n",
    "    event, values = window.read()\n",
    "    window.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_stats(df):\n",
    "    stats = df.describe().T\n",
    "    header_list = list(stats.columns)\n",
    "    data = stats.values.tolist()\n",
    "    for i,d in enumerate(data):\n",
    "        d.insert(0,list(stats.index)[i])\n",
    "    header_list=['Feature']+header_list\n",
    "    layout = [\n",
    "        [sg.Table(values=data,\n",
    "                  headings=header_list,\n",
    "                  font='Helvetica',\n",
    "                  pad=(10,10),\n",
    "                  display_row_numbers=False,\n",
    "                  auto_size_columns=True,\n",
    "                  num_rows=min(25, len(data)))]\n",
    "    ]\n",
    "\n",
    "    window = sg.Window(\"Statistics\", layout, grab_anywhere=False)\n",
    "    event, values = window.read()\n",
    "    window.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fig(df):\n",
    "    \"\"\"\n",
    "    Plots\n",
    "    \"\"\"\n",
    "    fig = plt.figure(dpi=125)\n",
    "    x = list(df.columns)[3]\n",
    "    y = list(df.columns)[5]\n",
    "    fig.add_subplot(111).scatter(df[x],df[y], color='blue',edgecolor='k')\n",
    "    plt.xlabel(x)\n",
    "    plt.ylabel(y)\n",
    "    plt.show(block=True)\n",
    "\n",
    "    # ------------------------------- END OF YOUR MATPLOTLIB CODE -------------------------------\n",
    "\n",
    "    # ------------------------------- Beginning of Matplotlib helper code -----------------------\n",
    "\n",
    "    def draw_figure(canvas, figure):\n",
    "        figure_canvas_agg = FigureCanvasTkAgg(figure, canvas)\n",
    "        figure_canvas_agg.draw()\n",
    "        figure_canvas_agg.get_tk_widget().pack(side='top', fill='both', expand=1)\n",
    "        return figure_canvas_agg\n",
    "\n",
    "    # ------------------------------- Beginning of GUI CODE -------------------------------\n",
    "\n",
    "    # define the window layout\n",
    "    layout = [[sg.Text('Plot test')],\n",
    "              [sg.Canvas(key='-CANVAS-', \n",
    "                         size=(750,520),\n",
    "                         pad=(25,25))],\n",
    "              [sg.Button('Ok')]]\n",
    "\n",
    "    # create the form and show it without the plot\n",
    "    window = sg.Window('Plot', \n",
    "                       layout,\n",
    "                       size=(800,600),\n",
    "                       finalize=True, \n",
    "                       element_justification='center', \n",
    "                       font='Helvetica 18')\n",
    "\n",
    "    # add the plot to the window\n",
    "    fig_canvas_agg = draw_figure(window['-CANVAS-'].TKCanvas, fig)\n",
    "\n",
    "    event, values = window.read()\n",
    "\n",
    "    window.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():   \n",
    "    # Reads the data\n",
    "    df,data, header_list,fn=read_table()\n",
    "\n",
    "    # Show data?\n",
    "    show_prompt = sg.popup_yes_no('Show the dataset?')\n",
    "    if show_prompt=='Yes':\n",
    "        show_table(data,header_list,fn)\n",
    "\n",
    "    # Show stats?\n",
    "    stats_prompt = sg.popup_yes_no('Show the descriptive stats?')\n",
    "    if stats_prompt=='Yes':\n",
    "        show_stats(df)\n",
    "\n",
    "    # Show a plot?\n",
    "    plot_prompt = sg.popup_yes_no('Show a scatter plot?')\n",
    "    if plot_prompt=='Yes':\n",
    "        plot_fig(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
